the us navy’s extended-range prediction system with high ... › event › 127 › contributions...

The US Navy’s Extended-range Prediction System with High-resolution Ocean and Ice Models

Carolyn Reynolds, Neil Barton, Maria Flatau, Sergey Frolov, Matthew Janiga, Justin McLay,

James Ridout, Ben Ruston, Timothy Whitcomb: NRL Marine Meteorology, Monterey, CA,

USA

Patrick Hogan, Gregg Jacobs, E. Joseph Metzger, Clark Rowley, Jay Shriver, Prasad Thoppil,

Rick Allard: NRL Oceanography, Stennis Space Center, MS, USA

James Richman: Florida State University, Tallahassee, FL, USA

Ole Martin Smetstad: Perspecta, Stennis Space Center, MS, USA

Andrew Huang: SAIC, Monterey, CA USA

Image from NASA: Internal waves over the Sulu Sea 1

Contact info: [email protected]

Outline

• US Navy ESPC: Motivation and Description

• Background on High-resolution Ocean Modeling• Importance of diurnal cycle for MJO

• Ocean eddies and boundary currents

• Bathymetry and internal tides

• Navy ESPC Performance• Deterministic (High-Res) 0-16 days (ocean, ice,

atmosphere)

• Ensemble (Lower-Res) to 60 days (ocean, ice,

atmosphere)

• Summary and Future Work


Outline






atmosphere)


atmosphere)



Extended-range Prediction Plays a Critical Role in DoD/Navy Planning and Policy

US Navy Operational Planning• Mission planning (e.g., typhoon risk assessment, ship routing)

• Long-term infrastructure installation and replacement planning

4

• US Navy has a long history of Arctic Ocean operations and explorations

• Reduced summer sea ice will make Arctic Ocean viable for international shipping and resource

explorations, and critical for national security concerns

• Hazardous environmental conditions make exploration and operations challenging

US Navy Arctic Roadmap: 2014-2030

US Navy Climate Change Task Force

US Navy S&T Strategic Plan

Match environmental predictive capabilities to tactical planning requirements: Fully coupled (ocean-

atmosphere-wave-ice) global, regional and local modeling and prediction capabilities for operational

planning at tactical, strategic, and climate scales

NRL supports US

Icebreaker Healy on

Geotraces mission to

the North Pole.

Typhoon Cobra, or Halsey’s

Typhoon, DEC1944. Three destroyers

and 790 lives lost.

National Earth System Prediction Capability

From Carmen et al. 2017: The National

Earth System Prediction Capability.

Coordinating the Giant. BAMS

Ship routing, prepositioning.

Plan humanitarian assistance,

manage force deployments.

5

Prediction Timescales

Hours to Days SeasonsWeeks-Months

Navy Need

Mesoscale and Global Weather Models

Climatology and Historical Analogs (Example: ENSO)

Are Used

NavyCapability

Fleet Safety and Operational Readiness

Long-Range Planning for Training Exercises

and Intelligence

Ship Routing, Force Positioning, Operational

Preparedness, Situational Awareness

Navy Earth System Prediction Capability

(New Capability => New Products)

6

US Navy ESPC Global Coupled System

• Developed to meet Navy needs for global earth system forecasts on timescales from days to months

• Navy ESPC team: NRL Monterey CA, NRL Stennis MS, NRL DC, NOAA ESMF

• Participating in NOAA SubX (subseasonal experiment): 45-day forecasts produced 4xweek, 1999-

present, archive for research and system evaluation, used in real time by National Ice Center for

resupply mission and field campaign planning

7

US Navy ESPC Initial Operational Capability

• IOC (late 2019 or early 2020): Weakly-coupled DA, perturbed obs in update cycle for ensembles

• Final Operational Capability: FY23 (seasonal forecasts, interactive ocean surface waves)

• Will not replace stand-along atmosphere system (NAVGEM) due to latency issues

• Stand-alone NAVGEM: 7 min/fcst day on 256 cores

• Navy ESPC: 1 hour/fcst day on 3000 cores

ForecastTime Range,

Frequency

Atmosphere

NAVGEM

Ocean

HYCOM

Ice

CICE

Waves

WW3

Deterministic

short term

0-16 days,

Daily

T681L60

(19 km)

60 levels

1/25°

(4.5 km)

41 layers

1/25°

(4.5 km)

1/8°

(14 km)

Probabilistic

long term

0-45 days

16 members

once per week

T359L60

(37 km)

60 levels

1/12°

(9 km)

41 layers

1/12°

(9 km)

1/4°

(28 km)

8

Uniqueness of US Navy ESPC:High Resolution Ocean and Sea Ice

US Navy needs high-fidelity simulations in atmosphere, ocean and sea-ice

9

Outline






atmosphere)


atmosphere)



Importance of Diurnal Cycle and Coupling for the MJO

DeMott et al., 2015: Atmosphere-ocean coupled processes in the Madden Julian Oscillation,

Reviews of Geophysics.

11

• Impact of coupling on MJO forecast skill:

• Improvements from dynamic ocean, 1-d mixed layer

models. Woolnough et al. 2007, Vitart et al. 2007, Seo et al. 2009; Fu et al.

20013, Shelly et al. 2014, Kim et al. 2010, Pegion and Kirtman 2008.

• Effects of coupling model dependent. Klingaman and

Woolnough 2014, Crueger et al. 2013.

• Two key barriers to MJO simulation in coupled GCMs:

• Poor simulation of response of upper ocean to atmospheric forcing

• Diurnal cycle important. Bernie et al. 2005, Shinoda 2005.

• MJO simulations improved with better diurnal cycle. Bernie et al. 2008, Klingaman et al. 2011, Ham et

al. 2014, Seo et al. 2014.

• Large systematic errors in tropical SSTs and circulation

• Mean state fidelity influences MJO representation. Sperber et al. 2005, Zhang et al. 2006.

• Mean state errors could impact “perceived effects” of subseasonal air-sea

interactions. Klingaman and Woolnough (2014).

Climate.gov drawing by Fiona Martin

Why High Resolution?Energetic Small Scales

12

2-year 1/50o HYCOM

surface current speed

from Eric Chassignet,

FSU.

Lots of energy

in small-scale

eddies, but

evolution fairly

slow: In ocean,

need high

resolution, but

time-to

completion

constraints not

as severe.

Why High Resolution?Small Rossby Radius of Deformation

13

Hallberg 2013: Using a resolution function to regulate parameterizations of oceanic mesoscale eddy effects. Ocean Modeling.

Rossby radius of

deformation is much

smaller in the ocean than

the atmosphere.

Ocean eddies (the

“weather” of the ocean)

about 10-100 km vs.

atmospheric weather

systems, 100-1000 km.

Why High Resolution?Impact of Ocean Eddies on Atmosphere

14

Saravanan and Chang, 2019: Midlatitude Mesoscale Ocean-Atmosphere Interaction and its Relevance to S2S Prediction.

Sub-seasonal to Seasonal Prediction (Robertson and Vitart, editors), and references therein.

• Mid-latitude basin scale: SST - wind speed negative correlation, ocean responds to atmosphere

• 10-1000 km scale: SST - wind speed positive correlation, atmosphere responds to ocean

• Ocean mesoscale eddies (OMES) evolve over several weeks, could impact atmospheric S2S

predictability

Storm-track eddy variance over Kuroshio extension (shaded),

and eddy variance difference when OME filtered out (contours)

27-km res 162-km res.Storm track eddy

variance decreases

throughout

troposphere when

OMEs are suppressed.

Also impact moisture

flux, diabatic heating,

downstream

circulations.

This effect is

mostly absent

when

atmospheric

resolution too

coarse to

resolve

processes on

OME scales.

Why High Resolution?Gulf Stream Separation

15

Marzocchi et al, 2015: The North Atlantic subpoloar circulation in an

eddy-resolving global ocean model. J. Marine Systems.1o

1/4o

1/12o

Observed SST Model - observations

Is 1/10o to 1/12o enough? Depends on if you

are concerned about interior ocean.

Gulf Stream in lower-res simulations doesn’t

separate from coast at right latitude, doesn’t extend

far enough eastward

1/12o ocean substantially better than 1o or 1/4o in

capturing extension of Gulf Stream into North Atlantic

Why High Horizontal Resolution?Gulf Stream

16

Chassignet and Xu, 2017: Impact of horizontal resolution (1/12o to 1/50o) on Gulf Stream separation, penetration, and variability. J.

Physical Oceanography.

Sea Surface Heights (cm)

1/50o

1/12o

Obs

1/25o

1/12o does not

extend far

enough

eastward

1/25o has

unrealistically

strong

recirculation

gyre southeast

of Cape Hatteras

Nonlinear

effects of

submesoscale

eddies

intensify

midlatitude jet

and increases

eastward

penetration

1/50o ocean

model best-

captures Gulf

Stream

properties

Why High Resolution?Gulf Stream Deep Ocean Circulation

17

From Hewitt et al. 2017: Will high-resolution global ocean models benefit coupled predictions on short-range to climate time-scales,

Ocean Modelling. Adapted from Chassignet and Xu (2017).

EKE (cm2s-2) along 55°W in the 3-year

long mooring measurements of

Richardson (1985) and for the 1/50°,

1/25°, and 1/12° HYCOM simulations

1/50o best-captures penetration of high

EKE into deep ocean

Caution about interpretation of

uncoupled simulations

Why High Horizontal Resolution?Gulf Stream Separation

18

Many studies on Gulf Stream separation:

• Continental slope steepening: Schoonover et la., 2017: Local Sensitivities of the Gulf Stream Separation. J. Physical

Oceanography.

• Representation of steep slopes by different vertical grids: Ezer, 2016: Revisiting the problem of the Gulf Stream

separation: on the representation of topography in ocean models with different types of vertical grids. Ocean Modelling.

Ocean current feedback, through

eddy-killing effect, stabilizes the

Gulf Stream separation and post-

separation. Renault et al., 2016: Control

and stabilization of the Gulf Stream by

oceanic current interactions with the

atmosphere. J. Physical Oceanography.

Why High Horizontal Resolution?Influence of Bathymetry

19

Hogan and Hurlburt, 2000: Impact of upper-ocean-topographical coupling

and isopyycnal outgropping in Japan/East Sea models with 1/8o to 1/64o

Resolution. Dynamics of Atmospheres and Oceans.1/8o 1/16o

1/64o1/32o

1/32o and 1/64o capture realistic East Korean Warm

Current separation at 38 N

Mean SSH for different resolution ocean simulations.

Why High Horizontal Resolution?Influence of Bathymetry

20

Mean SSH for different resolution ocean simulations.

Hogan and Hurlburt, 2000: Impact of upper-ocean-topographical coupling

and isopyycnal outgropping in Japan/East Sea models with 1/8o to 1/64o

Resolution. Dynamics of Atmospheres and Oceans.1/8o 1/16o

1/64o1/32o 1/32o Flat Bath

Bathymetric ridge off coast of the Asian mainland

plays key roll in separation of East Korean Warm

Current through upper ocean-bathymetric

coupling.

Bathymetry also important for internal tides.

Why High Resolution?Internal Tides

21

The addition of astronomical tidal forcing generates internal gravity waves at tidal

frequencies. Also known as internal tides, they are generated by large-scale barotropic flow

over bathymetric features that generate vertical motion.

Internal Tides in Navy ESPC 1/25o HYCOM

48-h animation

NASA and Global Ocean Associates

Why High Resolution?Internal Tides

22

Arbic, B.K., et al., 2012: Global modeling of internal tides within an

eddying ocean general circulation model. Oceanography.

Tidal flow over bathymetric features generate internal tides

• Displacement amplitudes > 50 m, current speeds > 2 m s-1

• In certain regions, tides important component of SSH variability

• Tides contribute about 80% of SSH variance measured by

altimeters (sometimes noise, sometimes signal)

Wavenumber spectrum of SSH (1/12o HYCOM)

Kuroshio, low-frequency (non-

tidal) motions dominate

Near Hawaii, high-frequency

(tidal) motions dominate

Large Scale Small Scale

Outline






atmosphere)


atmosphere)



US Navy ESPC Performance

• Deterministic ESPC Forecasts: T618 (19km) NAVGEM, 1/25o HYCOM, CICE v4,

• 16 day forecasts every week, 5 day forecasts every 5 days for 2017, verified against

• Generalized Digital Environmental Model (GDEM4) climatology and/or persistence

• Global Ocean Forecast System (GOFS) v3.5 (1/25o HYCOM, CICE v5)

• GOFS v3.1 (1/12o Ocean CICE v4)

• Operational NAVGEM forecasts

• GOFS systems bias correct the surface forcing from NAVGEM. ESPC system not bias corrected

• Ensemble ESPC Forecasts: T359 (37km) NAVGEM, 1/12o HYCOM, CICV v4

• 60-day forecasts run once/week for 2017

• Initial states derived from parallel update cycles with random observation errors

• Compared to NAVGEM, other S2S and SubX systems, persistence, and/or climatology

• Weakly-coupled Data Assimilation (background forecasts are fully-coupled)

• NAVDAS-AR (4DVAR) system used for Atmosphere

• NCODA (3DVAR) system used for ocean and ice

24

25

Temperature Mean Error and RMSE for the Globe against unassimilated profile observations

Navy ESPC (1/25o HYCOM)GOFS v3.1 (1/12o HYCOM)GOFS v3.5 (1/25o HYCOM)

For 4 to 6-day forecasts, coupled system has smaller biases near surface

US Navy Deterministic ESPC PerformanceShort-term Temperature Errors

26

Temperature Mean Error and RMSE for the Gulf

Stream region against unassimilated profile

observations

2 day fcst 4 day fcst 6 day fcst

Navy ESPC (1/25o HYCOM)

GOFS v3.1 (1/12o HYCOM)


For 6-day forecasts, 1/12o model has

larger mean error below 300 m than the

other two (1/25o) systems

US Navy Deterministic ESPC PerformanceShort-term Temperature Errors

US Navy Deterministic ESPC PerformanceGulf Stream Region

27

Gulf Stream temperature RMSE as a function of

forecast time for the globe as measured against

unassimilated observations

Navy ESPC (1/25o HYCOM) 16 d




GDEM4 Climatology

Persistence

0 – 50 m 50 – 150 m

150 – 500 m 8 – 500 m

0 5 10 15 0 5 10 15

All models beat climatology to 15 days

All models beat persistence past 6 days in

Gulf Stream Region (not true in all

regions)

US Navy Deterministic ESPC Performance15-m Current Errors

28

Monthly speed ME, RMSE, and vector correlation at

analysis time vs. independent drifting buoys at 15 m depth

Higher-resolution models have larger RMSE

errors in Gulf Stream region (could be sampling

or RMSE “penalty” at higher resolution)





US Navy Deterministic ESPC PerformanceIce Concentration

1/25o Sea Ice Thickness 30-day Integration from 20190711

Difficult to verify, but consistent with limited NASA IceBridge measurements29

US Navy Deterministic ESPC PerformanceImpact of CICE Version

Navy ESPC w/CICE v4.0 vs.

GOFS 3.5 with CICE v5.1.2

GOFS

ConcentrationNavy ESPC

Concentration

GOFS

ThicknessNavy ESPC

Thickness

GOFS 12 h Error

Navy ESPC 12 h Error

GOFS 36 h Error

Navy ESPC 36 h Error

Arctic Ice Edge Error

Winter Freeze-up

Navy ESPC (with CICE v4) has larger errors during

winter freeze-up in Arctic compared to GOFS 3.5 (with

CICE v5.1.2, better surface thermodynamics with melt

ponds, snow cover)

30

US Navy Deterministic ESPC PerformanceAtmospheric Performance

31

10-m wind speed biases (shading) and wind vector errors

(vectors) for operation NAVGEM (top) and Navy ESPC (bottom)

averaged for the first 7 days as verified against ECMWF analysis.

NAVGEM modified physics: MJO forecasts improved, but

slight degradations in 500-hPa height and some other

upper-air metrics

Operational NAVGEM

Navy ESPC Deterministic

Degradation in skill off the coast of Antarctica and eastern

Indian Ocean

Navy ESPC 10-m winds show improved performance over

most of the tropics, western boundary current regions

(similar results for buoy comparisons)

US Navy Ensemble ESPC PerformanceOcean Temperature and Salinity

32

Ensemble mean beats climatology

for temperature past 30 days

Control Member Ensemble Mean GDEM Climatology

8-500m Temperature 8-500 m Salinity

Ensemble mean beats climatology

for salinity past 20 days

US Navy Ensemble ESPC PerformanceDepth of Isotherms

33

RMSE, BIAS, ensemble STDV, for depth of 26o, 20o and 15o

isotherms (solid for Navy ESPC, dashed for GDEM climo). 26o

20o

15o

Ensemble mean beats or is comparable to climatology

out to about 60 days.

Ensemble forecasts are under-dispersive. Improving

ensemble design is a top priority.

US Navy Ensemble ESPC PerformanceTemperature Spatial Plots of Skill

34

Temperature 8-500 m SST

Day at which ensemble mean crosses the climatological RMSE

• Large spatial variations in skill relative to climatology

• “Smart” climo (e.g., conditioned on ENSO) would be more difficult to beat

US Navy Ensemble ESPC PerformanceSea Ice

2017 Integrated Ice Edge Error (IIE) vs average forecast day

Integrated Ice Edge Error

Goessling et al. (2016)

GRL)

Overestimate (blue) +

Underestimate (red)

ESPC ensemble mean

Persistence

Climatology

Predictability longer for Brier score for 15% ice concentration, but seasonally dependent

ESPC outperforms

climatology out to

32 (40) days in the

Arctic (Antarctic)

ESPC outperforms

persistence past 5

(for all) days in the

Arctic (Antarctic)

35

Predictability of Arctic Sea Ice Edge

Wayand et al, 2019: A year-round subseasonal-to-seasonal sea ice prediction portal. GRL.

BS for Sea Ice Concentration > 15% at 0,

1, 2, 3, 4 week lead times

Most models beat

damped anomaly

fcst after first week

Most models beat

damped anomaly and

climo out to week 4

Evidence of dependence on

initialization technique

RES

0.25o

0.25o

1o

0.25o

1o

0.5o

10 km

25-90 km

0.5-0.8o

0.2o

3.5 km

3.5 km

3.5 km

0.25o

Ens #

51

51

12

4

51

16

1

4

2

4

1

1

1

1

36

SubX Real-time Forecasts used by National Ice Center

37

US Navy Ensemble ESPC PerformanceMJO

38

Distribution of days when individual forecast MJO correlation (RMM1) drops below 0.6 (ensemble mean denoted by “x”)

Improvement in

MJO due to

physics changes

in Navy ESPC over

NAVGEM

Individual forecasts very skillful but ensemble skill does not match some other centers due to

ensembles being under-dispersive (ensemble design a priority)

US Navy Ensemble ESPC PerformanceTeleconnections

39

Navy ESPC “in the mix” for teleconnection

forecasts, very good for AAO and PNA.

But we gain less than other centers when using

ensemble mean.

Distribution of days where individual forecast teleconnection index

correlation drops below 0.6 (ensemble mean denoted by “x”)

US Navy Ensemble ESPC PerformanceEnsemble under-dispersive

40

Navy ESPC ensemble forecasts are currently under-dispersive. Work is underway to improve

ensemble design.

NAO Individual Member Skill

Navy ESPC

EC16

EC51

NAO Ensemble Mean Skill

Navy ESPC does not

gain as much going

from individual to

ensemble forecasts as

other systems,

particularly ECMWF

US Navy Ensemble ESPC PerformanceTesting Methods to Account for Model Uncertainty

NH Extratropics: 500mb height bias NH Extratropics: 10m wind speed spread-skill

baseline

baseline

Methods to

improve

ensemble spread

include relaxation

to prior

perturbation,

analysis

correction-based

additive inflation,

and SKEB

best result when all 3 are used

best result when all 3 are used

41

US Navy Ensemble ESPC Performance:Model Uncertainty in the Coupled System

HYCOM resolution typically ~4

times finer than NAVGEM

resolution. Currently using SST

averaged over atmospheric grid cell.

• Use a “gust” parameterization (e.g., Cheng et al. JGR, 2012) to

supply stronger winds to ocean

• Pass ocean SST variance (vs. mean) to atmosphere

• Have different ocean grid-points provide the SST for different

atmosphere ensemble members

• Also testing stochastic forcing in ocean and ice

Mismatch in Atmosphere/Ocean resolution

42

NRL Summary

• Operational transition scheduled for late 2019 or early 2020

• Relatively high resolution ocean ice models (1/12o for ensembles, 1/25o for deterministic)

• Latency issues preclude replacement of stand-alone atmospheric forecasts

• SubX runs being used by National Ice Center for resupply missions and field campaigns

• Continue to improve system for next system update in 2023 (CICE upgrade, ensemble design

improvements, interactive surface waves, tides in ensemble configuration)

• Develop products useful on S2S timescales with outreach to decision makers

43


44


Overall Summary and Future Directions

• High-resolution important for ocean simulations

• Evidence for ocean and ice predictability on multi-week timescales

• Impact of above on atmospheric predictability needs further study: Hewitt et al. (2017)

recommendations include

• Systematic studies with traceable model resolution hierarch

• Studies to identify relative importance of atmosphere vs. ocean resolution

• Modelling protocols to better quantify benefits of resolution

• Trade space between component resolution, latency, ensemble size very metric dependent, may

complicate unified system concept

• Solutions may include running ocean DA update cycle at high-resolution, but run long coupled

forecasts with lower-res ocean for atmospheric metrics, and higher-res ocean for ocean metrics.

• Ocean models candidates for static mesh refinement (e.g., Finite Element Ocean Model, Sein et al.

2017, JAMES)

Extra Slides

45

Representing the MJO –Moistening/Rainfall Relationship

Mean net moistening rate versus rainfall rate based on the

ECMWF-YOTC analysis and TRMM rainfall.

Klingaman et al. (2015) identified a particular “process-oriented diagnostic” as a good

indicator of MJO skill: the vertical distribution of total moistening as it varies with rainfall

rate in the Indo-Pacific warm pool region:

Subsidence

drying

Convective and Dynamical

(uplift) moistening

adapted from Klingaman

et al. 2015

10 Oct 2009 – 15 Feb

2010

Improving Warm Pool Moistening/Rainfall in Navy ESPC

The representation of the MJO in the Navy Global Environmental Model (NAVGEM) was seriously

deficient due to poor treatment of moistening/rainfall in the tropical warm pool. Research at NRL

significantly improved this key deficiency through implementation of a modified version of the Kain-

Fritsch convection scheme (Ridout, publication in preparation), providing much improved MJO skill.

The key modifications of the scheme responsible for the improvements are:

1) Addition of convective momentum transport

2) Enhanced entrainment into updrafts

3) Enhanced cloud-top sensitivity to environmental moisture

4) Bimodal treatment – both turbulence- and dynamically-forced modes

ESPC System with Modified KF20 Day Hindcast from 1 Nov 2011

ECMWF-YoTC and TRMM10 Oct 2009 – 15 Feb 2010

The DYNAMO period in 2011 has served as a development test case.

DYNAMO Case Study 61-Day Hindcasts from 1 Nov, 2011

TRMM

Satellite

Retrieval

40E 140E 40E 140E mm

Nov 1

Jan

1

Eq

uato

rial (5

oN

-5

oS

) P

rop

ag

ati

on

of

Rain

fall

Coupled Physics

Version 1 (CV1)

40E 140E40E 140E

CV2 CV3 prototypeModified cloud top condition

and trigger for turbulence-

forced convection mode

Modified coupling of

dynamically-forced convection

with grid-scale vertical motion

US Navy Deterministic ESPC Performance

49

Comparison of 10-m wind speed and 2-m temperature biases

compared to fixed buoys (and land surface stations)

Navy ESPC shows improved performance in

tropical winds and 2-m temperature,

comparable performance in NH winds, and

worse performance in SH wind bias.


NAVGEM (uncoupled)


50

10-m wind speed bias magnitude for operational NAVGEM

(black) and Navy ESPC (red) as verified against ECMWF

analysis as a function of forecast time.

Navy ESPC shows improved performance in

tropics, some degradation in SH, and

comparable performance in NH.


51

Global isotherm depth errors

relative to observations as a

function of forecast time.

All models beat

or match

climatology





GDEM Climatology

ME RMSE MAE


52

Monthly speed ME, RMSE, and vector correlation at

analysis time vs. independent drifting buoys at 15 m depth.

Higher-resolution models have

larger mean error and RMSE.




Why High Resolution?Deep Ocean Circulations

53

Thoppil et al., 2011, Energetics of a global ocean circulation model compared to observations, GRL

Experiment Name Experiment

1/12.5o FR 1/12.5o HYCOM Free Run (2005-2009)

1/25o FR 1/25o HYCOM Free Run (2005-2009)

1/12.5o DA 1/12.5o HYCOM with Data Assimilation (2008-2009)

Verification Level Dominant Energetics Validation

Surface Mean flow instabilities and direct

wind forcing

Drifting buoys (can accumulate in

regions of week flow)

150 m (below wind-driven mixed layer) Quasi-geostrophy Geostrophic estimates from

altimeter SSH (biased low)

1000 m (near thermocline) Quasi-geostrophy Subsurface drift vectors from

ARGO floats (biased low)

3000 m (abyssal ocean) Eddies generated by interaction

of mean flow with bathymetry

Moored current meter records

Why High Resolution?Deep Ocean Circulations

54

Thoppil et al., 2011, Energetics of a global ocean circulation model compared to observations. GRL.

Surface 150 m 1000 m Abyssal

1/12o Free Run 343 121 26.4 13.3

1/25o Free Run 423 181 37.9 18.3

1/12o Data Assim. 393 123 33.7 14.2

Obs 436 159 27.5 17.7

Model and Observed Eddy Kinetic Energy (cm2 s-2)

• In general 1/25o FR is the best match, but 1/12o DA improvement above free run

• Surface and abyssal ocean circulations strongly coupled through energy cascades that vertically

redistribute energy and vorticity throughout entire water column

EKE 23% – 49%

greater in 1/25o

FR over 1/12o

FR

1/12o DA has

13% increase

in EKE over

1/12o FR at

surface, 22%

increase at

1000 m

Why High Horizontal Resolution?Gulf Stream Deep Ocean Circulation

55

Thoppil et al., 2011, Energetics of a

global ocean circulation model

compared to observations. GRL.

Is 1/25o enough?

Surface AND abyssal

EKE better with 1/25o

FR OR 1/12o DA than

1/12o FR, with too little

EKE east of 60o W.

1/25o has too much EK

off Cape Hatteras

Why High Horizontal Resolution?

56

Chassignet and Xu, 2017: Impact of horizontal resolution (1/12o to 1/50o) on Gulf Stream separation, penetration, and variability. J.

Physical Oceanography

1/50o ocean model best-

captures Gulf Stream

properties: nonlinear effects

of submesoscale eddies

intensified midlatitude jet

and increases eastward

penetration.

Time Mean Surface Geostrophic Current (cm2 s-1)

1/50o

1/12o Obs

1/25o

Probability of Sea Ice > 15%

57

2+

weeks

3+

weeks

5+

weeks

7+

weeks

Navy ESPC ensemble

forecasts from

20170308 (shading).

NSIDC Verification

(solid)

Climatology (dashed)

Antarctic ice forecasts

beating climatology and

persistence out to

about 50 days (extra

slides)

the us navy’s extended-range prediction system with high ... › event › 127 › contributions...

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